Companies or any other business entities offer goods or services to at least one client. Business transactions have to be processed, and—especially for larger companies with many customers—the transactions are processed by multiple computer systems. In such systems, recurrent execution of similar processing operations is common. Therefore, the automation of business processes by utilizing various computer systems often involves recurrent executions of large numbers of similar operations. For example, the daily purchases in a chain of retail stores pile up to be settled by mass posting of the corresponding records to the company's general ledger and contractor's accounts. Usually, the mass activities, e.g., processing of similar type operations, impact the performance and availability of the computer systems of the companies. One common approach to facilitate mass activities is by parallelizing. The volume of atomic operations to be executed is broken into subsets or portions, and the different portions of operations are executed simultaneously by different computer system processes threads.
Generally, the parallelization of mass activities is a very efficient method to optimize the performance of the computer systems. However, the parallel execution requires careful analysis of the mass operations before splitting them in intervals to guarantee data consistency during the parallel processing, and to avoid futile competition for shared resources or deadlocks. Additionally, the mass activity operations have to be evenly portioned to assure parallel run during the entire execution time. Usually, the current methods for parallel mass processing involve extensive preprocessing to identify safe and efficient grouping of the operations. Alternatively, some computer system solutions apply simple parallelization criteria for grouping the mass operations, e.g., based on contract accounts. However, such simple solutions do not provide equal distribution of the operations, especially for recurrent mass processing scenarios. For example, the number of transactions attributed to different contractors may differ drastically in a mass activity session, as well as between different mass activity sessions.